A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Money and Prices in the Philippines, 1981-1992: A Cointegration Analysis

Based largely on the work of Funke and Hall, estimation results indicate non-causality between money and price level attributed to the interplay of factors such as unstable political and economic environment. P* vector has no significance on potential output since Q instead of Q* has been used.monetary aggregates, causality, price level

Money and Prices in the Philippines, 1981-1992: A Cointegration Analysis

Based largely on the work of Funke and Hall, estimation results indicate non-causality between money and price level attributed to the interplay of factors such as unstable political and economic environment. P* vector has no significance on potential output since Q instead of Q* has been used.monetary aggregates, causality, price level

Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study

Seasonal-to-decadal predictions are initialized using observations of the present climatic state in full field initialization (FFI). Such model integrations undergo a drift toward the model attractor due to model deficiencies that incur a bias in the model. The anomaly initialization (AI) approach reduces the drift by adding an estimate of the bias onto the observations at the expense of a larger initial error. In this study FFI is associated with the fidelity paradigm, and AI is associated with an instance of the mapping paradigm, in which the initial conditions are mapped onto the imperfect model attractor by adding a fixed error term; the mapped state on the model attractor should correspond to the nature state. Two diagnosis tools assess how well AI conforms to its own paradigm under various circumstances of model error: the degree of approximation of the model attractor is measured by calculating the overlap of the AI initial conditions PDF with the model PDF; and the sensitivity to random error in the initial conditions reveals how well the selected initial conditions on the model attractor correspond to the nature states. As a useful reference, the initial conditions of FFI are subjected to the same analysis. Conducting hindcast experiments using a hierarchy of low-order coupled climate models, it is shown that the initial conditions generated using AI approximate the model attractor only under certain conditions: differences in higher-than-first-order moments between the model and nature PDFs must be negligible. Where such conditions fail, FFI is likely to perform better

A new 2D static hand gesture colour image dataset for ASL gestures

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset

Expansion coefficient of the pseudo-scalar density using the gradient flow in lattice QCD

We use the Yang-Mills gradient flow to calculate the pseudo-scalar expansion coefficient $c_P^*(t_f)$. This quantity is a key ingredient to obtaining the chiral condensate and strange quark content of the nucleon using the Lattice QCD formulation, which can ultimately determine the spin independent (SI) elastic cross section of dark matter models involving WIMP-nucleon interactions. The goal, using the gradient flow, is to renormalize the chiral condensate and the strange content of the nucleon without a power divergent subtraction. Using Chiral symmetry and the small flow time expansion of the gradient flow, the scalar density at zero flow time can be related to the pseudo-scalar density at non zero flow time. By computing the flow time dependance of the pseudo-scalar density over multiple lattices box sizes, lattice spacings and pion masses, we can obtain the scalar density of the nucleon. Our lattice ensembles are $N_{f}=2+1$, PCAC-CS gauge field configurations, varying over $m_{\pi}\approx \{410,570,700\}$~MeV at $a=0.0907$~fm, with additional ensembles that vary $a\approx \{0.1095,0.0936,0.0684\}$~fm at $m_{\pi} \approx 700$~MeV